Clinical Cytology (5 ECTS)

Learning outcomes

Learning outcomes
As a student you recognise the significance of cytological tests in the diagnostics of diseases and in the treatment of patients. You identify different cell types based on cytological cell morphology and can distinguish between normal and atypical cells in key sample types according to the criteria of malignancy. You are able to justify the significance of clinical cytology tests in treating patients, diagnosing diseases and monitoring treatment. You are familiar with certain cytological tests and their indications, know how to perform these tests and are able to explain the role of different working phases as part of high-quality treatment.

Content

Contents
Principles of working in a pathology laboratory: current legislation and occupational safety, quality management, knowledge of information technology
Gynaecological and general cytological sampling methods
Cytological preparation techniques
Basic staining techniques
Normal morphology of key cell types and identification of typical cell findings
Criteria of malignancy

Prerequisites

Prior competence
Anatomy, Physiology and Pathophysiology, Immunology, Pre-analytics, Chemistry, Mathematics and Physics, Immunology, Methods in Laboratory Work, Cell Biology, Biochemistry and Basics of Medicine.

Teaching methods

Activating lectures via remote access. It is desirable to take lecture notes during the course.
Independent study of the course material is an essential part of the course.Online materials will also be used during the course. In the laboratory the theory learned in the lectures will be applied and deepened. The teaching may also include returnable assignments, which will be assessed on a pass/fail basis.
Independent assignments in Moodle/Teams to enhance learning.

Location and time

Teaching times are listed in the timetable.

Learning materials and recommended literature

Learning material:
Moodle Material
BioDigi Clinical Cytology (Moodle Material).

Dehqanzada Z. 2012. Kuvallinen opetusmateriaali gynekologisen irtosolunäytteen levyepiteelimuutoksista ja muutoksiin vaikuttavista tekijöistä. https://www.theseus.fi/bitstream/handle/10024/51172/NEW%20OK%201.pdf?sequence=1

Saeidpoor R & Seyyed Mansour Ayyoubi: Keuhkon sytologia -oppimateriaali :Normaalit, benignit ja malignisolut bronkusimu- ja harjanäytteissä http://www.theseus.fi/bitstream/handle/10024/51167/Saeidpoor_Roghiyeh.%20Ayyoubi_Seyyed_Mansour.pdf?sequence=1
Additional materials:
Naistentaudit E-kirja. Duodecim. Oppiportti.
Patologia E-kirja, Duodecim. Oppiportti.
Koivuniemi Ari. (toim.) Kliininen sytologia 1994:
• p.1-98 (Gynekologinen sytologia)
• p.143–166 (Hengityselinten sytologia)
• p. 269-296 (Virtsan sytologia)
p. 345-423 Ohutneulabiopsia

Alternative completion methods of implementation

No optional mode.

Internship and working life connections

N/A

Exam dates and retake possibilities

Final examination of the course
29.8 at 8.45-12.00 Exam at Myllypuro campus
Two re-exams :
Exam pass mark
The course grade can be increased once in the exam session following the first pass.

International connections

N/A

Student workload

Theory 2,5 cr
Practical studies 2,5 cr

Content scheduling

N/A

Further information for students

Guided laboratory assignments are compulsory 100%.
To pass the whole course the student must pass all assignments and the final exam.
If the student fail to pass the course she/he must participate to complet the whole course during next coming couse.
Students must confirm their attendance for a course implementation by being present when tuition begins. Students who are unable to attend the first teaching session due to illness may also confirm their attendance by informing the lecturer of this in advance.

Instructions on the use of artificial intelligence during the course are given separately. Students should familiarise themselves with Metropolia's AI guidelines and follow them in their study attainments.
Arene's traffic flow model or
- Using AI in teaching, learning and learning activities - A working guide for teachers, tutors and students (pdf file)
- Arene's recommendations on the use of AI for universities of applied sciences

Assessment methods and criteria

The grade of the course is determined by the final exam.
The final exam will be graded 0-5.
Learning tasks will be assessed : pass/fail
The course grade may be increased once in the examination period following the first pass.

If not all the performances have been completed by the 2nd re-examination, the implementation will be evaluated as failed and the student will have to register for a new implementation.
Also, if the student has not returned the assigned tasks by the 2nd re-examination date, the student's performance will be considered failed.

In the new implementation, the student must complete all parts of the implementation again, including the labs. The learning tasks must also The grade of the course is determined by the final exam.
The final exam will be graded 0-5.
Learning tasks will be assessed : pass/fail
The course grade may be increased once in the examination period following the first pass.

If not all the performances have been completed by the 2nd re-examination, the implementation will be evaluated as failed and the student will have to register for a new implementation.
Also, if the student has not returned the assigned tasks by the 2nd re-examination date, the student's performance will be considered failed.

In the new implementation, the student must complete all parts of the implementation again, including the labs. The learning tasks must also be completed again if they are different in the next implementation. If the learning tasks passed are the same as those passed in the failed section, they may be accepted for the new implementation.

Evaluation scale

0-5

Assessment criteria, satisfactory (1)

Assessment criteria
0–5
Assessment framework, see the appendix.
Assessment criteria for advanced level are applied.